In many circumstances, datasets can be incomplete or tainted by the presence of invalid data. For example, a sensor may have failed to record a data, or recorded an invalid value. The
numpy.ma module provides a convenient way to address this issue, by introducing masked arrays.Masked arrays are arrays that may have missing or invalid entries.
numpy.MaskedArray.any() function returns True if any of the elements of masked array evaluate to True.Masked values are considered as False during computation.
numpy.MaskedArray.any(axis=None, out=None, keepdims)
axis : [None or int or tuple of ints, optional] Axis or axes along which a logical AND reduction is performed.
out : [ndarray, optional] A location into which the result is stored.
-> If provided, it must have a shape that the inputs broadcast to.
-> If not provided or None, a freshly-allocated array is returned.
keepdims : [ bool, optional] If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.
Return : [bool or ndarray]A new boolean or ndarray is returned unless out is specified, in which case a reference to out is returned.
Code #1 :
Input array : [ 1 2 3 -1 5] Masked array : [1 2 -- -1 5] Output array : True
Code #2 :
Input array : [ 1 20 30 40 50] Masked array : [-- -- -- -- --] Output array : --
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